My research is focused on applying machine learning techniques to interpret flow rate, pressure and temperature data from Permanent Downhole Gauges (PDGs). The idea is to develop a methodology to describe the pattern behind flow rate, pressure and temperature data, which contain the reservoir information implicitly. By inputting a simple flow rate history, we are able to deconvolve the pressure signal as in conventional well testing. This approach utilizes PDG data for reservoir model identification without requiring additional operations, e.g. well shut-in.